Background/Rationale: Stroke remains the leading cause of long-term disability in our Veterans and the vast majority of stroke survivors experience significant motor impairments in the upper extremity. Survivors of stroke regain the most function during the early phases of recovery, a process that is likely mediated by changes in underlying neurophysiology. These changes, however, are not well understood in relation to voluntary reaching movements, but are nonetheless critical to successful rehabilitation. Electroencephalography (EEG) is one of several methods to understand the mechanisms underlying recovery in stroke, but EEG-based investigations have mainly relied on global brain electrical activity (i.e. during resting state) t correlate with motor impairment and/or function. Our goal, therefore, is to better understand the neural mechanisms during planning and execution of volitional reach across critical periods of recovery. We intend to use EEG to identify changes in brain-derived biomarkers that reflect the timing and direction of the intent to reach, and determine how these change over the course of natural recovery, i.e., as stroke survivors progress from the subacute to chronic phases. Objectives: Our preliminary data indicate that it is feasible to collect quality EEG in survivors o stroke as they volitionally perform motor tasks. Moreover, we detected brain-derived biomarkers that reliably encoded for aspects of reach, i.e., the intended direction to move and timing of movement onset. Specifically the first study demonstrated that when using the proposed task a brain-derived biomarker could be extracted which encoded for the intended direction of reach. The second study revealed that using this same task, a chronic stroke patient exhibited well-characterized cortical dynamics associated with volitional movement. Further, using these biomarkers as input to a Bayesian classifier robustly predicted the timing of the intent to move on a trial- by-trial basis. Thus, the classification will only improve when many more brain-derived biomarkers are available to this classification system with regard to timing and direction. In lin with these preliminary studies, our objective is to characterize the neurophysiological signals that best predict the onset and direction of volitional reaching movements in survivors of stroke, and determine how these identified signals change as stroke survivors recover from subacute to chronic stages. Methods: A convenience sample of forty stroke patients will be studied in the subacute phase of recovery (2-6 weeks post-stroke) and followed to the chronic stage (> 6 months.) At each visit, participants will be asked to choose between, and reach to, one of two targets in an InMotion planar robot while EEG data are collected concurrently. The EEG time series corresponding to the period prior to movement onset will be extracted. These data will be processed in order to extract a multitude of functional EEG metrics (e.g., spectral power, coherence, and movement related cortical potentials.) These metrics will be used as inputs into a Bayesian classifier to determine which biomarkers reliably encode for volitional timing and direction of intended movement. Finally, the changes in these biomarkers as a function of recovery will be determined. Findings/Results: We predict that we will identify a subset of biomarkers from the multitude of EEG metrics that will consistently indicate the intended direction and timing of volitional movement. Further, we predict that these biomarkers will change as a function of recovery with regard to timing and location, and will therefore provide both mechanistic insights to recovery as well as data relevant to brain-computer interfaces. Status: This project is currently in the planning and development stage. Impact: This research will have a significant impact on steering future work aimed at maximizing functional recovery. Identifying the brain-derived biomarkers encoding for specific components of reach in stroke survivors will reveal the neurophysiology underlying natural recovery and guide future rehabilitation strategies.